A02 - Graphical Exploration

---

Revised

21 May 2023

---

Programming Environment

packages <- c(
  'COUNT',
  'dcData',
  'esquisse',
  'mosaic',
  'rmarkdown',
  'tidytuesdayR',
  'tidyverse'
)

# Install packages not yet installed
installed_packages <- packages %in% rownames(installed.packages())
if (any(installed_packages == FALSE)) {
  install.packages(packages[!installed_packages])
}

# Load packages
invisible(lapply(packages, library, character.only = TRUE))

sessionInfo()

Loading required package: msme

Loading required package: MASS

Loading required package: lattice

Loading required package: sandwich

Registered S3 method overwritten by 'mosaic':
  method                           from   
  fortify.SpatialPolygonsDataFrame ggplot2

The 'mosaic' package masks several functions from core packages in order to add 
additional features.  The original behavior of these functions should not be affected by this.

Attaching package: ‘mosaic’

The following objects are masked from ‘package:dplyr’:

    count, do, tally

The following object is masked from ‘package:Matrix’:

    mean

The following object is masked from ‘package:ggplot2’:

    stat

The following objects are masked from ‘package:stats’:

    binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test,
    quantile, sd, t.test, var

The following objects are masked from ‘package:base’:

    max, mean, min, prod, range, sample, sum

── Attaching core tidyverse packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ lubridate 1.9.2     ✔ tibble    3.2.1
✔ purrr     1.0.2     ✔ tidyr     1.3.0
✔ readr     2.1.4     

── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ mosaic::count() masks dplyr::count()
✖ purrr::cross()  masks mosaic::cross()
✖ mosaic::do()    masks dplyr::do()
✖ tidyr::expand() masks Matrix::expand()
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
✖ tidyr::pack()   masks Matrix::pack()
✖ dplyr::select() masks MASS::select()
✖ mosaic::stat()  masks ggplot2::stat()
✖ mosaic::tally() masks dplyr::tally()
✖ tidyr::unpack() masks Matrix::unpack()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

R version 4.3.0 (2023-04-21)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 14.4.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] lubridate_1.9.2    forcats_1.0.0      stringr_1.5.0      purrr_1.0.2       
 [5] readr_2.1.4        tidyr_1.3.0        tibble_3.2.1       tidyverse_2.0.0   
 [9] tidytuesdayR_1.0.2 rmarkdown_2.22     mosaic_1.8.4.2     mosaicData_0.20.3 
[13] ggformula_0.10.4   dplyr_1.1.2        Matrix_1.5-4       ggplot2_3.4.3     
[17] esquisse_1.1.2     dcData_0.1.0       COUNT_1.3.4        sandwich_3.0-2    
[21] msme_0.5.3         lattice_0.21-8     MASS_7.3-58.4     

loaded via a namespace (and not attached):
 [1] writexl_1.4.2       tidyselect_1.2.0    IRdisplay_1.1      
 [4] farver_2.1.1        fastmap_1.1.1       tweenr_2.0.2       
 [7] promises_1.2.0.1    labelled_2.11.0     digest_0.6.31      
[10] timechange_0.2.0    mime_0.12           lifecycle_1.0.3    
[13] ellipsis_0.3.2      magrittr_2.0.3      compiler_4.3.0     
[16] rlang_1.1.1         sass_0.4.6          tools_4.3.0        
[19] utf8_1.2.3          data.table_1.14.8   knitr_1.43         
[22] htmlwidgets_1.6.2   curl_5.0.2          shinybusy_0.3.1    
[25] ggstance_0.3.6      xml2_1.3.4          repr_1.1.6         
[28] pbdZMQ_0.3-9        foreign_0.8-84      withr_2.5.0        
[31] shinyWidgets_0.7.6  grid_4.3.0          polyclip_1.10-4    
[34] mosaicCore_0.9.2.1  fansi_1.0.4         xtable_1.8-4       
[37] colorspace_2.1-0    scales_1.2.1        ggridges_0.5.4     
[40] cli_3.6.1           crayon_1.5.2        datamods_1.4.0     
[43] generics_0.1.3      rstudioapi_0.15.0   tzdb_0.4.0         
[46] httr_1.4.6          readxl_1.4.2        cachem_1.0.8       
[49] ggforce_0.4.1       rvest_1.0.3         cellranger_1.1.0   
[52] base64enc_0.1-3     vctrs_0.6.3         jsonlite_1.8.5     
[55] hms_1.1.3           jquerylib_0.1.4     rio_0.5.29         
[58] glue_1.6.2          stringi_1.7.12      gtable_0.3.3       
[61] later_1.3.1         munsell_0.5.0       pillar_1.9.0       
[64] htmltools_0.5.5     IRkernel_1.3.2      reactable_0.4.4    
[67] R6_2.5.1            evaluate_0.21       shiny_1.7.4        
[70] haven_2.5.2         openxlsx_4.2.5.2    httpuv_1.6.11      
[73] bslib_0.5.0         phosphoricons_0.1.2 Rcpp_1.0.10        
[76] zip_2.3.0           uuid_1.1-0          xfun_0.39          
[79] fs_1.6.2            usethis_2.1.6       zoo_1.8-12         
[82] pkgconfig_2.0.3    

---

glyph (mark, symbol) - the basic graphical unit, often corresponding to a case

• e.g., scatter, density, bar, etc.

aesthetic - a visual property of a glyph (e.g., position, size, shape, color, etc.)

• may be mapped, based on the data values (e.g., sex -> color)

• may be set, or fixed to arbitrary non-data values (color=blue)

scale - a mapping that translates data values to aesthetics

frame - the position scale, describing how data are mapped to the coordinate system

• What are the axis limits?

• What kind of scale?: linear, logarithmic, etc.

guide - the legend, helpful for the reader to translage aesthetics back to data values

• axis ticks, axis labels; legend; labels

facet

layer

stat

data('iris')
iris.glyphready <- iris %>%
  rename(x=Sepal.Length,y=Petal.Length,color=Species)
head(iris.glyphready)
ggplot(data=iris.glyphready,aes(x=x,y=y,color=as.factor(color))) +
  geom_point() +
  geom_smooth(se=TRUE)
ggplot(data=iris,aes(x=Petal.Width)) +
  geom_histogram(bins=15)

A data.frame: 6 × 5

	x	Sepal.Width	y	Petal.Width	color
	<dbl>	<dbl>	<dbl>	<dbl>	<fct>
1	5.1	3.5	1.4	0.2	setosa
2	4.9	3.0	1.4	0.2	setosa
3	4.7	3.2	1.3	0.2	setosa
4	4.6	3.1	1.5	0.2	setosa
5	5.0	3.6	1.4	0.2	setosa
6	5.4	3.9	1.7	0.4	setosa

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

A02

data('diamonds',package='ggplot2')
diamonds

A tibble: 53940 × 10

carat	cut	color	clarity	depth	table	price	x	y	z
<dbl>	<ord>	<ord>	<ord>	<dbl>	<dbl>	<int>	<dbl>	<dbl>	<dbl>
0.23	Ideal	E	SI2	61.5	55	326	3.95	3.98	2.43
0.21	Premium	E	SI1	59.8	61	326	3.89	3.84	2.31
0.23	Good	E	VS1	56.9	65	327	4.05	4.07	2.31
0.29	Premium	I	VS2	62.4	58	334	4.20	4.23	2.63
0.31	Good	J	SI2	63.3	58	335	4.34	4.35	2.75
0.24	Very Good	J	VVS2	62.8	57	336	3.94	3.96	2.48
0.24	Very Good	I	VVS1	62.3	57	336	3.95	3.98	2.47
0.26	Very Good	H	SI1	61.9	55	337	4.07	4.11	2.53
0.22	Fair	E	VS2	65.1	61	337	3.87	3.78	2.49
0.23	Very Good	H	VS1	59.4	61	338	4.00	4.05	2.39
0.30	Good	J	SI1	64.0	55	339	4.25	4.28	2.73
0.23	Ideal	J	VS1	62.8	56	340	3.93	3.90	2.46
0.22	Premium	F	SI1	60.4	61	342	3.88	3.84	2.33
0.31	Ideal	J	SI2	62.2	54	344	4.35	4.37	2.71
0.20	Premium	E	SI2	60.2	62	345	3.79	3.75	2.27
0.32	Premium	E	I1	60.9	58	345	4.38	4.42	2.68
0.30	Ideal	I	SI2	62.0	54	348	4.31	4.34	2.68
0.30	Good	J	SI1	63.4	54	351	4.23	4.29	2.70
0.30	Good	J	SI1	63.8	56	351	4.23	4.26	2.71
0.30	Very Good	J	SI1	62.7	59	351	4.21	4.27	2.66
0.30	Good	I	SI2	63.3	56	351	4.26	4.30	2.71
0.23	Very Good	E	VS2	63.8	55	352	3.85	3.92	2.48
0.23	Very Good	H	VS1	61.0	57	353	3.94	3.96	2.41
0.31	Very Good	J	SI1	59.4	62	353	4.39	4.43	2.62
0.31	Very Good	J	SI1	58.1	62	353	4.44	4.47	2.59
0.23	Very Good	G	VVS2	60.4	58	354	3.97	4.01	2.41
0.24	Premium	I	VS1	62.5	57	355	3.97	3.94	2.47
0.30	Very Good	J	VS2	62.2	57	357	4.28	4.30	2.67
0.23	Very Good	D	VS2	60.5	61	357	3.96	3.97	2.40
0.23	Very Good	F	VS1	60.9	57	357	3.96	3.99	2.42
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
0.70	Premium	E	SI1	60.5	58	2753	5.74	5.77	3.48
0.57	Premium	E	IF	59.8	60	2753	5.43	5.38	3.23
0.61	Premium	F	VVS1	61.8	59	2753	5.48	5.40	3.36
0.80	Good	G	VS2	64.2	58	2753	5.84	5.81	3.74
0.84	Good	I	VS1	63.7	59	2753	5.94	5.90	3.77
0.77	Ideal	E	SI2	62.1	56	2753	5.84	5.86	3.63
0.74	Good	D	SI1	63.1	59	2753	5.71	5.74	3.61
0.90	Very Good	J	SI1	63.2	60	2753	6.12	6.09	3.86
0.76	Premium	I	VS1	59.3	62	2753	5.93	5.85	3.49
0.76	Ideal	I	VVS1	62.2	55	2753	5.89	5.87	3.66
0.70	Very Good	E	VS2	62.4	60	2755	5.57	5.61	3.49
0.70	Very Good	E	VS2	62.8	60	2755	5.59	5.65	3.53
0.70	Very Good	D	VS1	63.1	59	2755	5.67	5.58	3.55
0.73	Ideal	I	VS2	61.3	56	2756	5.80	5.84	3.57
0.73	Ideal	I	VS2	61.6	55	2756	5.82	5.84	3.59
0.79	Ideal	I	SI1	61.6	56	2756	5.95	5.97	3.67
0.71	Ideal	E	SI1	61.9	56	2756	5.71	5.73	3.54
0.79	Good	F	SI1	58.1	59	2756	6.06	6.13	3.54
0.79	Premium	E	SI2	61.4	58	2756	6.03	5.96	3.68
0.71	Ideal	G	VS1	61.4	56	2756	5.76	5.73	3.53
0.71	Premium	E	SI1	60.5	55	2756	5.79	5.74	3.49
0.71	Premium	F	SI1	59.8	62	2756	5.74	5.73	3.43
0.70	Very Good	E	VS2	60.5	59	2757	5.71	5.76	3.47
0.70	Very Good	E	VS2	61.2	59	2757	5.69	5.72	3.49
0.72	Premium	D	SI1	62.7	59	2757	5.69	5.73	3.58
0.72	Ideal	D	SI1	60.8	57	2757	5.75	5.76	3.50
0.72	Good	D	SI1	63.1	55	2757	5.69	5.75	3.61
0.70	Very Good	D	SI1	62.8	60	2757	5.66	5.68	3.56
0.86	Premium	H	SI2	61.0	58	2757	6.15	6.12	3.74
0.75	Ideal	D	SI2	62.2	55	2757	5.83	5.87	3.64

?diamonds

options(repr.plot.width=20,repr.plot.height=30)

plt <- ggplot(data=diamonds) +
  geom_point(aes(x=carat,
                 y=price,
                 color=color,
                 #shape=clarity,
                 size=depth,
                 alpha=table)) +
  facet_wrap(clarity~cut,nrow=8) +
  theme(text=element_text(size=20))
plt

#suppressWarnings(print(plt))

# https://www.kaggle.com/datasets/crawford/80-cereals
cereal <- read_csv('cereal.csv')
cereal

Rows: 77 Columns: 16

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (3): name, mfr, type
dbl (13): calories, protein, fat, sodium, fiber, carbo, sugars, potass, vita...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

A spec_tbl_df: 77 × 16

name	mfr	type	calories	protein	fat	sodium	fiber	carbo	sugars	potass	vitamins	shelf	weight	cups	rating
<chr>	<chr>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
100% Bran	N	C	70	4	1	130	10.0	5.0	6	280	25	3	1.00	0.33	68.40297
100% Natural Bran	Q	C	120	3	5	15	2.0	8.0	8	135	0	3	1.00	1.00	33.98368
All-Bran	K	C	70	4	1	260	9.0	7.0	5	320	25	3	1.00	0.33	59.42551
All-Bran with Extra Fiber	K	C	50	4	0	140	14.0	8.0	0	330	25	3	1.00	0.50	93.70491
Almond Delight	R	C	110	2	2	200	1.0	14.0	8	-1	25	3	1.00	0.75	34.38484
Apple Cinnamon Cheerios	G	C	110	2	2	180	1.5	10.5	10	70	25	1	1.00	0.75	29.50954
Apple Jacks	K	C	110	2	0	125	1.0	11.0	14	30	25	2	1.00	1.00	33.17409
Basic 4	G	C	130	3	2	210	2.0	18.0	8	100	25	3	1.33	0.75	37.03856
Bran Chex	R	C	90	2	1	200	4.0	15.0	6	125	25	1	1.00	0.67	49.12025
Bran Flakes	P	C	90	3	0	210	5.0	13.0	5	190	25	3	1.00	0.67	53.31381
Cap'n'Crunch	Q	C	120	1	2	220	0.0	12.0	12	35	25	2	1.00	0.75	18.04285
Cheerios	G	C	110	6	2	290	2.0	17.0	1	105	25	1	1.00	1.25	50.76500
Cinnamon Toast Crunch	G	C	120	1	3	210	0.0	13.0	9	45	25	2	1.00	0.75	19.82357
Clusters	G	C	110	3	2	140	2.0	13.0	7	105	25	3	1.00	0.50	40.40021
Cocoa Puffs	G	C	110	1	1	180	0.0	12.0	13	55	25	2	1.00	1.00	22.73645
Corn Chex	R	C	110	2	0	280	0.0	22.0	3	25	25	1	1.00	1.00	41.44502
Corn Flakes	K	C	100	2	0	290	1.0	21.0	2	35	25	1	1.00	1.00	45.86332
Corn Pops	K	C	110	1	0	90	1.0	13.0	12	20	25	2	1.00	1.00	35.78279
Count Chocula	G	C	110	1	1	180	0.0	12.0	13	65	25	2	1.00	1.00	22.39651
Cracklin' Oat Bran	K	C	110	3	3	140	4.0	10.0	7	160	25	3	1.00	0.50	40.44877
Cream of Wheat (Quick)	N	H	100	3	0	80	1.0	21.0	0	-1	0	2	1.00	1.00	64.53382
Crispix	K	C	110	2	0	220	1.0	21.0	3	30	25	3	1.00	1.00	46.89564
Crispy Wheat & Raisins	G	C	100	2	1	140	2.0	11.0	10	120	25	3	1.00	0.75	36.17620
Double Chex	R	C	100	2	0	190	1.0	18.0	5	80	25	3	1.00	0.75	44.33086
Froot Loops	K	C	110	2	1	125	1.0	11.0	13	30	25	2	1.00	1.00	32.20758
Frosted Flakes	K	C	110	1	0	200	1.0	14.0	11	25	25	1	1.00	0.75	31.43597
Frosted Mini-Wheats	K	C	100	3	0	0	3.0	14.0	7	100	25	2	1.00	0.80	58.34514
Fruit & Fibre Dates; Walnuts; and Oats	P	C	120	3	2	160	5.0	12.0	10	200	25	3	1.25	0.67	40.91705
Fruitful Bran	K	C	120	3	0	240	5.0	14.0	12	190	25	3	1.33	0.67	41.01549
Fruity Pebbles	P	C	110	1	1	135	0.0	13.0	12	25	25	2	1.00	0.75	28.02576
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
Multi-Grain Cheerios	G	C	100	2	1	220	2.0	15.0	6	90	25	1	1.00	1.00	40.10596
Nut&Honey Crunch	K	C	120	2	1	190	0.0	15.0	9	40	25	2	1.00	0.67	29.92429
Nutri-Grain Almond-Raisin	K	C	140	3	2	220	3.0	21.0	7	130	25	3	1.33	0.67	40.69232
Nutri-grain Wheat	K	C	90	3	0	170	3.0	18.0	2	90	25	3	1.00	1.00	59.64284
Oatmeal Raisin Crisp	G	C	130	3	2	170	1.5	13.5	10	120	25	3	1.25	0.50	30.45084
Post Nat. Raisin Bran	P	C	120	3	1	200	6.0	11.0	14	260	25	3	1.33	0.67	37.84059
Product 19	K	C	100	3	0	320	1.0	20.0	3	45	100	3	1.00	1.00	41.50354
Puffed Rice	Q	C	50	1	0	0	0.0	13.0	0	15	0	3	0.50	1.00	60.75611
Puffed Wheat	Q	C	50	2	0	0	1.0	10.0	0	50	0	3	0.50	1.00	63.00565
Quaker Oat Squares	Q	C	100	4	1	135	2.0	14.0	6	110	25	3	1.00	0.50	49.51187
Quaker Oatmeal	Q	H	100	5	2	0	2.7	-1.0	-1	110	0	1	1.00	0.67	50.82839
Raisin Bran	K	C	120	3	1	210	5.0	14.0	12	240	25	2	1.33	0.75	39.25920
Raisin Nut Bran	G	C	100	3	2	140	2.5	10.5	8	140	25	3	1.00	0.50	39.70340
Raisin Squares	K	C	90	2	0	0	2.0	15.0	6	110	25	3	1.00	0.50	55.33314
Rice Chex	R	C	110	1	0	240	0.0	23.0	2	30	25	1	1.00	1.13	41.99893
Rice Krispies	K	C	110	2	0	290	0.0	22.0	3	35	25	1	1.00	1.00	40.56016
Shredded Wheat	N	C	80	2	0	0	3.0	16.0	0	95	0	1	0.83	1.00	68.23588
Shredded Wheat 'n'Bran	N	C	90	3	0	0	4.0	19.0	0	140	0	1	1.00	0.67	74.47295
Shredded Wheat spoon size	N	C	90	3	0	0	3.0	20.0	0	120	0	1	1.00	0.67	72.80179
Smacks	K	C	110	2	1	70	1.0	9.0	15	40	25	2	1.00	0.75	31.23005
Special K	K	C	110	6	0	230	1.0	16.0	3	55	25	1	1.00	1.00	53.13132
Strawberry Fruit Wheats	N	C	90	2	0	15	3.0	15.0	5	90	25	2	1.00	1.00	59.36399
Total Corn Flakes	G	C	110	2	1	200	0.0	21.0	3	35	100	3	1.00	1.00	38.83975
Total Raisin Bran	G	C	140	3	1	190	4.0	15.0	14	230	100	3	1.50	1.00	28.59278
Total Whole Grain	G	C	100	3	1	200	3.0	16.0	3	110	100	3	1.00	1.00	46.65884
Triples	G	C	110	2	1	250	0.0	21.0	3	60	25	3	1.00	0.75	39.10617
Trix	G	C	110	1	1	140	0.0	13.0	12	25	25	2	1.00	1.00	27.75330
Wheat Chex	R	C	100	3	1	230	3.0	17.0	3	115	25	1	1.00	0.67	49.78744
Wheaties	G	C	100	3	1	200	3.0	17.0	3	110	25	1	1.00	1.00	51.59219
Wheaties Honey Gold	G	C	110	2	1	200	1.0	16.0	8	60	25	1	1.00	0.75	36.18756

cereal %>%
  count(mfr) %>%
    arrange(desc(n))
cereal %>%
  count(type) %>%
    arrange(desc(n))
cereal %>%
  count(calories) %>%
    arrange(desc(n))
cereal %>%
  count(protein) %>%
    arrange(desc(n))
cereal %>%
  count(fat) %>%
    arrange(desc(n))

A spec_tbl_df: 7 × 2

mfr	n
<chr>	<int>
K	23
G	22
P	9
Q	8
R	8
N	6
A	1

A spec_tbl_df: 2 × 2

type	n
<chr>	<int>
C	74
H	3

A spec_tbl_df: 11 × 2

calories	n
<dbl>	<int>
110	29
100	17
120	10
90	7
50	3
140	3
70	2
130	2
150	2
80	1
160	1

A spec_tbl_df: 6 × 2

protein	n
<dbl>	<int>
3	28
2	25
1	13
4	8
6	2
5	1

A spec_tbl_df: 5 × 2

fat	n
<dbl>	<int>
1	30
0	27
2	14
3	5
5	1

options(repr.plot.width=20,repr.plot.height=10)

plt <- ggplot(data=cereal) +
  geom_point(aes(x=sugars,
                 y=rating,
                 #color=color,
                 shape=type)) +
                 #size=depth,
                 #alpha=table)) +
  facet_wrap(~mfr,nrow=2) +
  theme(text=element_text(size=30))
plt

#suppressWarnings(print(plt))

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